The Potential of Sentinel-2 Satellite Images for Land-Cover/Land-Use and Forest Biomass Estimation: A Review

Isbaex, Crismeire; Coelho, Ana Rita

doi:10.5772/intechopen.93363

Cited by 16 publications

(8 citation statements)

References 112 publications

(181 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Top-of-atmosphere (TOA) reflectance was converted to top-of-canopy (TOC) reflectance [57,58]. Sub-setting and mosaicking were carried out to produce a single image for the study area [59]. The S2 MSI (10 m) images were resampled to 20 m resolution to match the plot size (20 m) and this was done using the nearest neighbored resampling technique in ArcMap [59].…”

Section: Satellite Climatic and Topographic Variablesmentioning

confidence: 99%

“…Sub-setting and mosaicking were carried out to produce a single image for the study area [59]. The S2 MSI (10 m) images were resampled to 20 m resolution to match the plot size (20 m) and this was done using the nearest neighbored resampling technique in ArcMap [59]. This interpolation method was used because its processes are faster, the algorithm has less rigorous implementation procedures and it is suitable for discrete data such as AGB [60][61][62][63].…”

Section: Satellite Climatic and Topographic Variablesmentioning

confidence: 99%

See 1 more Smart Citation

Quantification of Above-Ground Biomass over the Cross-River State, Nigeria, Using Sentinel-2 Data

et al. 2022

View full text Add to dashboard Cite

Higher-resolution wall-to-wall carbon monitoring in tropical Africa across a range of woodland types is necessary in reducing uncertainty in the global carbon budget and improving accounting for Reducing Emissions from Deforestation and forest Degradation Plus (REDD+). This study uses Sentinel-2 multispectral imagery combined with climatic and edaphic variables to estimate the regional distribution of aboveground biomass (AGB) for the year 2020 over the Cross River State, a tropical forest region in Nigeria, using random forest (RF) machine learning. Forest inventory plots were collected over the whole state for training and testing of the RF algorithm, and spread over undisturbed and disturbed tropical forests, and woodlands in croplands and plantations. The maximum AGB plot was estimated to be 588 t/ha with an average of 121.98 t/ha across the entire Cross River State. AGB estimated using random forest yielded an R2 of 0.88, RMSE of 40.9 t/ha, a relRMSE of 30%, bias of +7.5 t/ha and a total woody regional AGB of 0.246 Pg for the Cross River State. These results compare favorably to previous tropical AGB products; with total AGB of 0.290, 0.253, 0.330 and 0.124 Pg, relRMSE of 49.69, 57.09, 24.06 and 56.24% and −41, −48, −17 and −50 t/ha bias over the Cross River State for the Saatchi, Baccini, Avitabile and ESA CCI maps, respectively. These are all compared to the current REDD+ estimate of total AGB over the Cross River State of 0.268 Pg. This study shows that obtaining independent reference plot datasets, from a variety of woodland cover types, can reduce uncertainties in local to regional AGB estimation compared with those products which have limited tropical African and Nigerian woodland reference plots. Though REDD+ biomass in the region is relatively larger than the estimates of this study, REDD+ provided only regional biomass rather than pixel-based biomass and used estimated tree height rather than the actual tree height measurement in the field. These may cast doubt on the accuracy of the estimated biomass by REDD+. These give the biomass map of this current study a comparative advantage over others. The 20 m wall-to-wall biomass map of this study could be used as a baseline for REDD+ monitoring, evaluation, and reporting for equitable distribution of payment for carbon protection benefits and its management.

show abstract

Section: Satellite Climatic and Topographic Variablesmentioning

confidence: 99%

Section: Satellite Climatic and Topographic Variablesmentioning

confidence: 99%

Quantification of Above-Ground Biomass over the Cross-River State, Nigeria, Using Sentinel-2 Data

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Remote sensors allow to get information of the vegetation state, through vegetation indices (VI) that are generated with the combination of spectral bands; these VI are correlated with several variables, such as biomass, density, volume and C ( Isbaex & Coelho, 2021 ). These variables are estimated by adjusting allometric models with data collected in the field based on the vegetation indices ( Chen et al, 2018 ; Pandit, Tsuyuki & Dube, 2018 ; Pertille et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Carbon mapping in pine-oak stands under timber management in southern Mexico

Ambrosio-Lazo,

Rodríguez-Ortiz,

Rincón-Ramírez

et al. 2023

PeerJ

View full text Add to dashboard Cite

The destructive and empirical methods commonly used to estimate carbon pools in forests managed timber are time-consuming, expensive and unfeasible at a large scale; satellite images allow evaluations at different scales, reducing time and costs. The objective of this study was to evaluate the tree biomass (TB) and carbon content (CC) through satellite images derived from Sentinel 2 in underutilized stands in southern Mexico. In 2022, 12 circular sites of 400 m2 with four silvicultural treatments (STs) were established in a targeted manner: 1st thinning (T1), free thinning (FT), regeneration cut (RC) and unmanaged area (UA). A tree inventory was carried out, and samples were obtained to determine their TB based on specific gravity and CC through the Walkey and Black method. The satellite image of the study area was downloaded from Sentinel 2 to fit a simple linear model as a function of the Normalized Difference Vegetation Index (10 m pixel−1) showing significance (p ≤ 0.01) and a adjusted R2 = 0.92. Subsequently, the TB and CC (t ha−1) were estimated for each ST and managed area. The total managed area (3,201 ha−1) had 126 t TB ha−1 and 57 t C ha−1. Of the areas with STs, the area with FT showed the highest accumulation of TB (140 t ha−1) and C (63 t ha−1) without showing differences (p > 0.05) with respect to those of the UA, which presented 129 t TB ha−1 and 58 t C ha−1. The satellite images from Sentinel 2 provide reliable estimates of the amounts of TB and CC in the managed stands. Therefore, it can be concluded that an adequate application of STs maintains a balance in the accumulation of tree C.

show abstract

“…Large territorial coverages make remote sensing technologies a particularly helpful tool for spatially mapping forest features, by enabling continuous predictions and extrapolations in areas where hand-collected data is unavailable [5]. Among the various active and passive sensors available for forest monitoring, satellite imagery is the most widespread due to its relative ease of acquisition and broad spatial and temporal scopes [25,26]. In the context of forest monitoring in China, satellite imagery has been increasingly used to estimate variables such as aboveground biomass [27], alpha diversity [28], and species presence and abundance [29], being the detection of forests' spatial drivers a less explored task.…”

Section: Introductionmentioning

confidence: 99%

Mapping and analyzing the spatial drivers of Chinese subtropical forests features from Gaofen 1 satellite imagery

González-Rodríguez,

2023

Preprint

View full text Add to dashboard Cite

Understanding the spatial variations of forest features and their underlying drivers is crucial for managing and preserving forest ecosystems. Remote sensing techniques, due to their vast spatial coverage, are proving an essential tool for mapping and analyzing the complex spatial patterns of forests characteristics. In the current study, we use high-resolution Gaofen 1 satellite imagery to map features, such as biomass, biodiversity, and habitat type, of the subtropical forests in the Nanling area, a major biodiversity hot spot in the south of China; for subsequently analyzing the association between forest features and environmental drivers. We based our modeling approach on a semi-supervised fully convolutional neural network consisting of a ResNet architecture and a masking and aggregation module for computing predictions for forest survey plots of varied sizes. The fitted convolutional models yielded very high performance statistics, with test R2 values above 0.90 for basal area, aboveground biomass and Shannon diversity index regressions, slightly lower values for the regression of species richness (R2=0.83), and average test classification accuracy of forest types of 0.89, with lower performance for infrequent forest types, such as dwarf forests. The association analysis between predicted maps of forest features and environmental drivers revealed significant correlations being the strongest drivers the altitude, nitrogen content and climate variables, such as mean annual precipitation and temperature. Altitude was strongly associated with dwarf forests and high basal area and high aboveground biomass locations. Basal area and aboveground biomass were also positively influenced by annual rainfall and soil nitrogen and carbon, and negatively by temperature and radiation variables. Species richness was associated with high nitrogen and carbon contents, while the Shannon diversity index showed overall mild correlations with most drivers, with no remarkable patterns. We concluded that our semi-supervised convolutional approach using Gaofen 1 imagery proved useful for modeling the spatial distribution of the subtropical forests in Nanling; however, more research and data collection is necessary to improve the performance of species richness modeling and segmentation of infrequent forest types. Moreover, the strong associations found between climate, and predicted maps of biomass and forest types, provided key insights into the potential repercussions of climate change on Nanling forests.

show abstract

The Potential of Sentinel-2 Satellite Images for Land-Cover/Land-Use and Forest Biomass Estimation: A Review

Cited by 16 publications

References 112 publications

Quantification of Above-Ground Biomass over the Cross-River State, Nigeria, Using Sentinel-2 Data

Quantification of Above-Ground Biomass over the Cross-River State, Nigeria, Using Sentinel-2 Data

Carbon mapping in pine-oak stands under timber management in southern Mexico

Mapping and analyzing the spatial drivers of Chinese subtropical forests features from Gaofen 1 satellite imagery

Contact Info

Product

Resources

About